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Chromatography hyphenated to high resolution mass spectrometry in untargeted metabolomics for investigation of food (bio)markers. Trends Analyt Chem 2021. [DOI: 10.1016/j.trac.2020.116161] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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Kuuliala L, Pérez-Fernández R, Tang M, Vanderroost M, De Baets B, Devlieghere F. Probabilistic topic modelling in food spoilage analysis: A case study with Atlantic salmon (Salmo salar). Int J Food Microbiol 2020; 337:108955. [PMID: 33186831 DOI: 10.1016/j.ijfoodmicro.2020.108955] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Revised: 08/10/2020] [Accepted: 10/25/2020] [Indexed: 10/23/2022]
Abstract
Probabilistic topic modelling is frequently used in machine learning and statistical analysis for extracting latent information from complex datasets. Despite being closely associated with natural language processing and text mining, these methods possess several properties that make them particularly attractive in metabolomics applications where the applicability of traditional multivariate statistics tends to be limited. The aim of the study was thus to introduce probabilistic topic modelling - more specifically, Latent Dirichlet Allocation (LDA) - in a novel experimental context: volatilome-based (sea) food spoilage characterization. This was realized as a case study, focusing on modelling the spoilage of Atlantic salmon (Salmo salar) at 4 °C under different gaseous atmospheres (% CO2/O2/N2): 0/0/100 (A), air (B), 60/0/40 (C) or 60/40/0 (D). First, an exploratory analysis was performed to optimize the model tunings and to consequently model salmon spoilage under 100% N2 (A). Based on the obtained results, a systematic spoilage characterization protocol was established and used for identifying potential volatile spoilage indicators under all tested storage conditions. In conclusion, LDA could be used for extracting sets of underlying VOC profiles and identifying those signifying salmon spoilage, giving rise to an extensive discussion regarding the key points associated with model tuning and/or spoilage analysis. The identified compounds were well in accordance with a previously established approach based on partial least squares regression analysis (PLS). Overall, the outcomes of the study not only reflect the promising potential of LDA in spoilage characterization, but also provide several new insights into the development of data-driven methods for food quality analysis.
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Affiliation(s)
- L Kuuliala
- Research Unit Food Microbiology and Food Preservation (FMFP), Department of Food Technology, Safety and Health, Part of Food2Know, Faculty of Bioscience Engineering, Ghent University, Coupure links 653, B-9000 Ghent, Belgium; Research Unit Knowledge-based Systems (KERMIT), Department of Data Analysis and Mathematical Modelling, Part of Food2Know, Faculty of Bioscience Engineering, Ghent University, Coupure links 653, B-9000 Ghent, Belgium.
| | - R Pérez-Fernández
- Research Unit Knowledge-based Systems (KERMIT), Department of Data Analysis and Mathematical Modelling, Part of Food2Know, Faculty of Bioscience Engineering, Ghent University, Coupure links 653, B-9000 Ghent, Belgium
| | - M Tang
- Research Unit Knowledge-based Systems (KERMIT), Department of Data Analysis and Mathematical Modelling, Part of Food2Know, Faculty of Bioscience Engineering, Ghent University, Coupure links 653, B-9000 Ghent, Belgium
| | - M Vanderroost
- Research Unit Food Microbiology and Food Preservation (FMFP), Department of Food Technology, Safety and Health, Part of Food2Know, Faculty of Bioscience Engineering, Ghent University, Coupure links 653, B-9000 Ghent, Belgium
| | - B De Baets
- Research Unit Knowledge-based Systems (KERMIT), Department of Data Analysis and Mathematical Modelling, Part of Food2Know, Faculty of Bioscience Engineering, Ghent University, Coupure links 653, B-9000 Ghent, Belgium
| | - F Devlieghere
- Research Unit Food Microbiology and Food Preservation (FMFP), Department of Food Technology, Safety and Health, Part of Food2Know, Faculty of Bioscience Engineering, Ghent University, Coupure links 653, B-9000 Ghent, Belgium
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Hernandez-Baixauli J, Quesada-Vázquez S, Mariné-Casadó R, Gil Cardoso K, Caimari A, Del Bas JM, Escoté X, Baselga-Escudero L. Detection of Early Disease Risk Factors Associated with Metabolic Syndrome: A New Era with the NMR Metabolomics Assessment. Nutrients 2020; 12:E806. [PMID: 32197513 PMCID: PMC7146483 DOI: 10.3390/nu12030806] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 03/11/2020] [Accepted: 03/17/2020] [Indexed: 02/07/2023] Open
Abstract
The metabolic syndrome is a multifactorial disease developed due to accumulation and chronification of several risk factors associated with disrupted metabolism. The early detection of the biomarkers by NMR spectroscopy could be helpful to prevent multifactorial diseases. The exposure of each risk factor can be detected by traditional molecular markers but the current biomarkers have not been enough precise to detect the primary stages of disease. Thus, there is a need to obtain novel molecular markers of pre-disease stages. A promising source of new molecular markers are metabolomics standing out the research of biomarkers in NMR approaches. An increasing number of nutritionists integrate metabolomics into their study design, making nutrimetabolomics one of the most promising avenues for improving personalized nutrition. This review highlight the major five risk factors associated with metabolic syndrome and related diseases including carbohydrate dysfunction, dyslipidemia, oxidative stress, inflammation, and gut microbiota dysbiosis. Together, it is proposed a profile of metabolites of each risk factor obtained from NMR approaches to target them using personalized nutrition, which will improve the quality of life for these patients.
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Affiliation(s)
- Julia Hernandez-Baixauli
- Eurecat, Centre Tecnològic de Catalunya, Unitat de Nutrició i Salut, 43204 Reus, Spain; (J.H.-B.); (S.Q.-V.); (R.M.-C.); (K.G.C.); (A.C.); (J.M.D.B.)
| | - Sergio Quesada-Vázquez
- Eurecat, Centre Tecnològic de Catalunya, Unitat de Nutrició i Salut, 43204 Reus, Spain; (J.H.-B.); (S.Q.-V.); (R.M.-C.); (K.G.C.); (A.C.); (J.M.D.B.)
| | - Roger Mariné-Casadó
- Eurecat, Centre Tecnològic de Catalunya, Unitat de Nutrició i Salut, 43204 Reus, Spain; (J.H.-B.); (S.Q.-V.); (R.M.-C.); (K.G.C.); (A.C.); (J.M.D.B.)
- Universitat Rovira i Virgili; Department of Biochemistry and Biotechnology, Ctra. De Valls, s/n, 43007 Tarragona, Spain
| | - Katherine Gil Cardoso
- Eurecat, Centre Tecnològic de Catalunya, Unitat de Nutrició i Salut, 43204 Reus, Spain; (J.H.-B.); (S.Q.-V.); (R.M.-C.); (K.G.C.); (A.C.); (J.M.D.B.)
- Universitat Rovira i Virgili; Department of Biochemistry and Biotechnology, Ctra. De Valls, s/n, 43007 Tarragona, Spain
| | - Antoni Caimari
- Eurecat, Centre Tecnològic de Catalunya, Unitat de Nutrició i Salut, 43204 Reus, Spain; (J.H.-B.); (S.Q.-V.); (R.M.-C.); (K.G.C.); (A.C.); (J.M.D.B.)
| | - Josep M Del Bas
- Eurecat, Centre Tecnològic de Catalunya, Unitat de Nutrició i Salut, 43204 Reus, Spain; (J.H.-B.); (S.Q.-V.); (R.M.-C.); (K.G.C.); (A.C.); (J.M.D.B.)
| | - Xavier Escoté
- Eurecat, Centre Tecnològic de Catalunya, Unitat de Nutrició i Salut, 43204 Reus, Spain; (J.H.-B.); (S.Q.-V.); (R.M.-C.); (K.G.C.); (A.C.); (J.M.D.B.)
| | - Laura Baselga-Escudero
- Eurecat, Centre Tecnològic de Catalunya, Unitat de Nutrició i Salut, 43204 Reus, Spain; (J.H.-B.); (S.Q.-V.); (R.M.-C.); (K.G.C.); (A.C.); (J.M.D.B.)
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McClements DJ. Future foods: a manifesto for research priorities in structural design of foods. Food Funct 2020; 11:1933-1945. [PMID: 32141468 DOI: 10.1039/c9fo02076d] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
A number of major challenges facing modern society are related to the food supply. As the global population grows, it will be critical to feed everyone without damaging the environment. Advances in biotechnology, nanotechnology, structural design, and artificial intelligence are providing farmers and food manufacturers will new tools to address these problems. More and more people are migrating from rural to urban environments, leading to a change in their dietary habits, especially increasing consumption of animal-based products and highly-processed foods. Animal-based foods lead to more greenhouse gas production, land use, water use, and pollution than plant-based ones. Moreover, many animal-based and highly-processed foods have adverse effects on human health and wellbeing. Consumers are therefore being encouraged to consume more plant-based foods, such as fruits, vegetables, cereals, and legumes. Many people, however, do not have the time, money, or inclination to prepare foods from fresh produce. Consequently, there is a need for the food industry to create a new generation of processed foods that are desirable, tasty, inexpensive, and convenient, but that are also healthy and sustainable. This article highlights some of the main food-related challenges faced by modern society and how scientists are developing innovative technologies to address them.
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Gibson R, Lau CHE, Loo RL, Ebbels TMD, Chekmeneva E, Dyer AR, Miura K, Ueshima H, Zhao L, Daviglus ML, Stamler J, Van Horn L, Elliott P, Holmes E, Chan Q. The association of fish consumption and its urinary metabolites with cardiovascular risk factors: the International Study of Macro-/Micronutrients and Blood Pressure (INTERMAP). Am J Clin Nutr 2020; 111:280-290. [PMID: 31782492 PMCID: PMC6997096 DOI: 10.1093/ajcn/nqz293] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2019] [Accepted: 10/30/2019] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Results from observational studies regarding associations between fish (including shellfish) intake and cardiovascular disease risk factors, including blood pressure (BP) and BMI, are inconsistent. OBJECTIVE To investigate associations of fish consumption and associated urinary metabolites with BP and BMI in free-living populations. METHODS We used cross-sectional data from the International Study of Macro-/Micronutrients and Blood Pressure (INTERMAP), including 4680 men and women (40-59 y) from Japan, China, the United Kingdom, and United States. Dietary intakes were assessed by four 24-h dietary recalls and BP from 8 measurements. Urinary metabolites (2 timed 24-h urinary samples) associated with fish intake acquired from NMR spectroscopy were identified. Linear models were used to estimate BP and BMI differences across categories of intake and per 2 SD higher intake of fish and its biomarkers. RESULTS No significant associations were observed between fish intake and BP. There was a direct association with fish intake and BMI in the Japanese population sample (P trend = 0.03; fully adjusted model). In Japan, trimethylamine-N-oxide (TMAO) and taurine, respectively, demonstrated area under the receiver operating characteristic curve (AUC) values of 0.81 and 0.78 in discriminating high against low fish intake, whereas homarine (a metabolite found in shellfish muscle) demonstrated an AUC of 0.80 for high/nonshellfish intake. Direct associations were observed between urinary TMAO and BMI for all regions except Japan (P < 0.0001) and in Western populations between TMAO and BP (diastolic blood pressure: mean difference 1.28; 95% CI: 0.55, 2.02 mmHg; P = 0.0006, systolic blood pressure: mean difference 1.67; 95% CI: 0.60, 2.73 mmHg; P = 0.002). CONCLUSIONS Urinary TMAO showed a stronger association with fish intake in the Japanese compared with the Western population sample. Urinary TMAO was directly associated with BP in the Western but not the Japanese population sample. Associations between fish intake and its biomarkers and downstream associations with BP/BMI appear to be context specific. INTERMAP is registered at www.clinicaltrials.gov as NCT00005271.
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Affiliation(s)
- Rachel Gibson
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- Department of Nutritional Sciences, School of Life Course Sciences, King's College London, London, United Kingdom
| | - Chung-Ho E Lau
- Division of Diabetes Endocrinology and Metabolism, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, United Kingdom
| | - Ruey Leng Loo
- Institute of Health Futures, Murdoch University, Perth, Western Australia, Australia
| | - Timothy M D Ebbels
- Division of System Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, United Kingdom
| | - Elena Chekmeneva
- National Institute for Health Research–British Research Council, Clinical Phenotyping Centre, Department of Surgery and Cancer, Imperial College London, London, United Kingdom
| | - Alan R Dyer
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Katsuyuki Miura
- Department of Public Health, Shiga University of Medical Science, Otsu, Shiga, Japan
| | - Hirotsugu Ueshima
- Department of Public Health, Shiga University of Medical Science, Otsu, Shiga, Japan
| | - Liancheng Zhao
- Department of Epidemiology, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Martha L Daviglus
- Institute for Minority Health Research, University of Illinois at Chicago, Chicago, IL, USA
| | - Jeremiah Stamler
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Linda Van Horn
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Paul Elliott
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- Health Data Research UK London, Imperial College London, London, United Kingdom
| | - Elaine Holmes
- Division of Diabetes Endocrinology and Metabolism, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, United Kingdom
- Institute of Health Futures, Murdoch University, Perth, Western Australia, Australia
| | - Queenie Chan
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
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